fix flash attention for mistral. (#758)

## fix flash attention for mistral. 

This pull request fixes flash attention forward method for mistral.

src/adapters/models/mistral/modeling_mistral.py

@@ -45,7 +45,7 @@
 
 
 if is_flash_attn_2_available():
-    from transformers.models.mistral.modeling_mistral import _flash_supports_window_size
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
 
 
 logger = logging.get_logger(__name__)
@@ -173,18 +173,6 @@ def forward(
         cos, sin = self.rotary_emb(value_states, position_ids)
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-        use_sliding_windows = (
-            _flash_supports_window_size
-            and getattr(self.config, "sliding_window", None) is not None
-            and kv_seq_len > self.config.sliding_window
-        )
-
-        if not _flash_supports_window_size:
-            logger.warning_once(
-                "The current flash attention version does not support sliding window attention, for a more memory"
-                " efficient implementation make sure to upgrade flash-attn library."
-            )
-
         if past_key_value is not None:
             # Activate slicing cache only if the config has a value `sliding_windows` attribute
             cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
@@ -257,14 +245,17 @@ def forward(
         key_states = key_states.transpose(1, 2)
         value_states = value_states.transpose(1, 2)
 
-        attn_output = self._flash_attention_forward(
+        attn_output = _flash_attention_forward(
             query_states,
             key_states,
             value_states,
             attention_mask,
             q_len,
+            position_ids=position_ids,
             dropout=dropout_rate,
-            use_sliding_windows=use_sliding_windows,
+            sliding_window=getattr(self.config, "sliding_window", None),
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
         )
 
         attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()